With the 2020s heralded as the "decade of action" in our collective pursuit of bringing about transformative change in the context of climate action, one thing is clear: technology is the linchpin in this journey towards a more sustainable and resilient future. Innovation has taken center stage, driving financial growth and catalysing strategies that can have a significant impact. At the heart of this technological revolution is Artificial Intelligence (AI), a force that has harnessed the immense power of machines to mimic human problem-solving and decision-making capabilities.

While the concept of AI has developed since the 1950s, several converging trends in recent years have propelled it to the forefront of our sustainable aspirations. The exponential growth in computing processing power, the advent of the cloud, and the deployment of advanced technologies such as high-resolution cameras, satllite systems, sensors, drones, and unmanned aerial vehicles (UAVs), all backed by vast computational space and storage, have created an unprecedented synergy. This potent amalgamation of technological advancements has catalysed a real-time application of AI technology across various domains, particularly in climate action.

Related: Read our blog “The Future of Climate Action: How AI Can Help Save the Planet” to learn more.

Specifically, they have collectively enabled Digital MRV (Measurement, Reporting, and Verification), a pivotal force in the sustainability journey.

Unpacking the Value of AI-driven Digital MRV

Digital MRV, which encompasses the vital processes of Monitoring or Measuring, Reporting, and Verification, is the backbone of our efforts to assess, track, and manage our environmental impact. The World Bank Group defines it as “the use of AI, machine learning, satellite imagery, blockchain, smart sensors, the internet of things (IoT), cloud computing, and drones in MRV systems to fully or partially automate data collection, recording, and processing for reporting and verification.”

The concept of MRV which was popularised at the Bali COP in 2007, continues to face significant challenges despite having been developed for over 25 years. These challenges include issues of inefficiency, time-consuming processes, cumbersome procedures, and relatively high costs, in addition to limitations in delivering real-time, detailed, and comprehensive insights necessary for informed decision-making.

The concept of digital MRV (dMRV) thus emerged in response to the need for more accurate and efficient methods of tracking and verifying emissions reductions. Technological advancements propelled this growth as they provided the tools and capabilities needed to improve the accuracy, efficiency, and comprehensiveness of environmental monitoring and reporting. Among these tools, AI specifically emerged as a pivotal catalyst that effectively bridges these persistent challenges in traditional MRV methods.

AI's ability to process vast datasets at unparalleled speeds has revolutionised how we collect and analyze environmental information. Whether it's monitoring the carbon footprint of industries, tracking deforestation in remote regions, or assessing the health of ecosystems, AI-driven Digital MRV systems excel in capturing, processing, and visualizing data in ways that were once inconceivable. This newfound precision empowers us to pinpoint areas of concern swiftly and take decisive actions when and where they are needed most.

Abhilasha Purwar, CEO of Blue Sky Analytics, believes that “Artificial Intelligence (AI) is a transformative paradigm, with its ability to collect, process, and interpret vast reams of data. With efficient use of data, algorithms, and remote sensing devices, AI can help stakeholders make informed and data-driven decisions, which is crucial in bringing down emissions and building a more sustainable future.”

Furthermore, AI's adaptive algorithms and machine learning capabilities allow Digital MRV systems to evolve with the changing landscape of environmental challenges as they adapt to new data sources, emerging patterns, and evolving threats. By doing so, AI-driven Digital MRV transcends the limitations of static, rule-based systems, providing us with a dynamic tool that keeps pace with the dynamic nature of our planet.

Some of the notable benefits of AI-driven dMRV include:

  • Enhanced accuracy: AI-driven Digital MRV can contribute to better accuracy compared to traditional methods as it can provide continuous real-time monitoring, as opposed to periodic or manual data collection. This ensures that changes and anomalies are detected promptly, reducing the chances of missing critical data points. AI algorithms can also automatically validate and quality-check incoming data, flagging or correcting errors and inconsistencies.

  • Big data analysis: Considering the synergistic relationship with Big data, AI excels at processing and analysing massive datasets quickly and efficiently. In the context of digital MRV, where vast amounts of environmental data are collected from various sources such as sensors and satellites, AI's ability to handle big data is invaluable. AI's capacity to make sense of and process vast amounts of data surpasses what traditional methods can achieve. This is especially important when dealing with the overwhelming quantity and complexity of environmental data, where AI can efficiently extract meaningful insights and patterns.

  • Increased efficiency: AI-driven Digital MRV are highly efficient in comparison to traditional MRV methods. Its value stems from automation, speed, scalability, and the ability to process and analyze vast amounts of data with minimal human intervention. These efficiencies can lead to more timely and informed decision-making, resource optimization, and improved environmental management, in addition to reducing costs in the long term.

  • Data Fusion: AI can integrate data from multiple sources such as sensors, satellites, and IoT devices, as well as from different formats and combine it into a unified dataset, thus filling gaps in information that may exist in individual datasets. This allows for a more comprehensive understanding of environmental conditions. Further, as different data sources may have varying degrees of accuracy and precision, data fusion methods can take into account the reliability of each data source and assign appropriate weights to them. This results in a more accurate representation of the monitored variables, as it reduces the impact of less accurate data sources.

  • Trend Analysis: AI can analyse historical data to identify long-term trends and patterns, helping predict future environmental changes. This allows organizations to take preemptive and proactive measures in response to changing conditions, whether tracking a gradual temperature increase or identifying shifts in air quality. It helps organisations make data-driven decisions, respond to changing environmental conditions, and work towards sustainability and compliance goals.

  • Enhanced Transparency and Accountability: By allowing real-time data access, it enables transparency in the process, which ensures that data is readily available for scrutiny. Furthermore, AI can cross-reference and verify data with other trusted sources, ensuring that reported information is accurate and consistent. This verification process adds an extra layer of transparency and accountability.

Applications of AI-driven Digital MRV

AI-driven dMRV has a wide range of uses. Notable ones include:

  • Carbon Emission Tracking: AI can analyze data from various sources, such as sensors and satellite imagery, to monitor and report carbon emissions accurately. This is crucial for organizations aiming to reduce their carbon footprint and comply with emissions regulations.

  • Supply Chain Resilience: CDP predicts companies could incur up to $120 billion in supply chain environmental risk costs from 2021 to 2026. By harnessing the power of Satellite data and AI, it is possible to undertake real-time environmental monitoring and predictive modeling, allowing companies to make informed decisions to safeguard their supply chains from climate-related disruptions. It also helps scale the approach of emerging concepts such as carbon insetting and unlocking its full potential in building a company’s supply chain resilience.

  • Natural Resource Management: AI-powered remote sensing and satellite imagery analysis can help monitor and report on the state of natural resources, such as forests, water bodies, and land use changes. It can also help assess the health of ecosystems by analyzing data on biodiversity, habitat changes, impact of climate change, etc., helping with conservation efforts. To learn more, read our blog “Enabling Nature-based Solutions in Global South with AI”.

  • Climate Change Mitigation: To model extreme weather events, a significant number of variables must be taken into account. AI is well-suited for this task, as it has the capability to collect, process, and analyse large datasets, enabling it to handle the complexity of the climate modelling processes effectively. It not only increases the accuracy of the models but also helps in creating better simulations. This further helps provide a clear picture of risks and, thus, by extension, aids in developing more effective adaptation and mitigation strategies. Read our blog “Harnessing Climate Intelligence to Tackle the Ongoing Climate Carnage” to learn more.

  • Compliance and Reporting: It plays a pivotal role in enhancing Compliance and Reporting by transforming financial processes, fortifying fraud detection, and ensuring rigorous adherence to regulatory obligations. Through automation and machine learning, AI speeds up data collection and analysis, reducing human errors and expediting financial reporting, thereby increasing efficiency and accuracy. Read our blog to learn how AI and satellite data can help in red-flagging environmental controversies.

  • Disaster management and preparedness: AI-driven digital MRV enhances disaster management and preparedness by rapidly analyzing data from various sources to detect early warning signs of potential disasters. It can process real-time information such as weather patterns to provide timely alerts and inform evacuation plans. Additionally, it assists responders by providing situational awareness, enabling more efficient resource allocation and response coordination during disaster events. Read our blog “The Role of Comprehensive Data in Efficient Climate Modeling” to learn more.

All these functions have made dMRV a crucial tool in areas such as asset monitoring, carbon markets, and climate-related financial risk assessments. Moreover, the digital MRV landscape is continuously evolving, with a proliferation of innovative solutions entering the market. As technology advances and computational capabilities grow, new applications continue to emerge, addressing the evolving challenges of climate change. These applications play a pivotal role in assisting organizations and governments in achieving sustainability goals, reducing their environmental impact, and making data-driven decisions.

How we Power dMRV with AI at Blue Sky Analytics

The inception of Blue Sky Analytics was rooted in the belief that data serves as a vital catalyst for advancing climate action. A significant challenge we encountered on this journey was the scarcity of consistent spatial and temporal data. In our mission to bridge these data gaps and capture a complete view of climate risks across the globe, we began to harness the potential of Artificial Intelligence. AI has since become the linchpin of our mission, empowering us to generate an extensive repertoire of environmental and climate datasets by leveraging satellite data, cloud computing, and machine learning.

Related: Read the article featuring our CEO, “Women in AI: Abhilasha Purwar,” to learn more about how we’ve leveraged AI for climate action.

Since our inception in 2018, we have made significant progress in a short period of time. Our journey began with air pollution monitoring in 2018, and today, we possess a range of datasets capable of monitoring and measuring various environmental factors, including the surface area of water bodies, wildfire prediction, monitoring GHG emissions from fires, and Power Plant Emissions, among others. Furthermore, we are slated to deliver several more datasets in the coming months, including electrification and Ground Water Potential, among others. By leveraging the potential of AI, we are committed to delivering innovative environmental solutions that have real-world impact.

Tree count estimates as visualised in Blue Sky Analytics’ visualisation platform- SpaceTime™.  Estimating tree count is integral to carbon sequestration projects as it provides the foundational data required for calculating, monitoring, and verifying carbon storage. It supports effective project planning, reporting, and resource management, ultimately contributing to the success of efforts to mitigate climate change through increased carbon sequestration in forests and wooded areas.

Tree count estimates as visualised in Blue Sky Analytics’ visualisation platform- SpaceTime™. Estimating tree count is integral to carbon sequestration projects as it provides the foundational data required for calculating, monitoring, and verifying carbon storage. It supports effective project planning, reporting, and resource management, ultimately contributing to the success of efforts to mitigate climate change through increased carbon sequestration in forests and wooded areas.

In today's rapidly changing world, understanding and managing the risks posed by climate change is more critical than ever. We believe deploying AI responsibly and ethically will be highly beneficial in shaping transformative policy decisions and fostering the transition to a more sustainable future.